MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases

Granlund, Tuomas; Kopponen, Aleksi; Stirbu, Vlad; Myllyaho, Lalli; Mikkonen, Tommi

doi:10.48550/arxiv.2103.08937

Cited by 3 publications

(2 citation statements)

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“…TRL assessments of some of the most important and representative AI technologies, as listed in [157], can help telecommunication operators better understand the state of the art of a particular technology when planning to adopt it in telecommunication infrastructure. The integration and scaling issues raised in [158] when deploying AI/ML in a cross-organizational context (e.g., between a hospital and a service provider) may be useful for telecommunication operators when integrating AI/ML platforms into their vertical industries [159], [160].…”

Section: A Gap Analysismentioning

confidence: 99%

Recent Advances in Data Engineering for Networking

Zeydan

Mangues‐Bafalluy

2022

IEEE Access

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Section: A Gap Analysismentioning

confidence: 99%

Recent Advances in Data Engineering for Networking

Zeydan

Mangues‐Bafalluy

2022

IEEE Access

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“…The GDPR, as a "regulation," applies to any or all Member States, with no obligation for transfer into national legislation. The GDPR came into effect in mid-2018, giving the public and private sectors time to adjust existing structural measures to the new regulations [23,24].…”

Section: Privacy and Securitymentioning

confidence: 99%

Application of MLOps in Prediction of Lifestyle Diseases

et al. 2022

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Our health is the consequence of a combination of intricate and stabilized interactions between genetic elements, microbiome configuration, environmental impulses, and lifestyle habits. Knowledge of their contributions, as well as the complex network that connects them, is essential to analyze the underlying mechanisms and the onset of many diseases and can provide crucial information on their prevention, diagnosis, and remedy. We have seen advancements in data analytic methods to discover valuable patterns by analyzing great amounts of non-standard, heterogeneous, unstructured, and incomplete healthcare data. Not only does it help in decision making, but also forecasting. With the limited accuracy identified in machine learning models, we have implemented a process framework of Machine Learning Operations (MLOps) to develop a robust and collaborative platform for streamlining data and process integration and synergy through the automation of retaining, testing, and deployment.

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An Analysis of the Barriers Preventing the Implementation of MLOps

Kolar Narayanappa,

Amrit

2023

IFIP Advances in Information and Communication Technology

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MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases

Cited by 3 publications

References 0 publications

Recent Advances in Data Engineering for Networking

Recent Advances in Data Engineering for Networking

Application of MLOps in Prediction of Lifestyle Diseases

An Analysis of the Barriers Preventing the Implementation of MLOps

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